Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.
Browser extensions have been established as a common feature present in modern browsers. However, some extension systems risk exposing APIs which are too permissive and cohesive with the browser's internal structure, thus leaving a hole for malicious developers to exploit security critical functionality within the browser itself. In this paper, we raise the awareness of the threats caused by browser extensions by presenting a botnet framework based on malicious extensions installed in the user's browser, and an exhaustive range of attacks that can be launched in this framework. We systematical…
Information security is the protection of information from a wide range of threats in order to ensure success business continuity by minimizing risks and maximizing the return of investments and business opportunities. In this paper, we study and discuss the software vulnerabilities, banking threats, botnets and propose the malware self-protection technologies.
Botnets, which consist of thousands of compromised machines, can cause significant threats to other systems by launching Distributed Denial of Service (SSoS) attacks, keylogging, and backdoors. In response to these threats, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Interface (API) functions calls made by communication applications and store these calls with their arguments in log files. Our algorithm detects botnets based on monitoring abnormal activity by correlating t…
Botnet as it is popularly called became fashionable in recent times owing to it embedded force on network servers. Botnet has an exponential growth of about 170, 000 within network server and client infrastructures per day. The networking environment on monthly basis battle over 5 million bots. Nigeria as a country loses above one hundred and twenty five (N125) billion naira to network fraud annually, end users such as Banks and other financial institutions battle daily the botnet threats.
Botnets are prevailing mechanisms for the facilitation of the distributed denial of service (DDoS) attacks on computer networks or applications. Currently, Botnet-based DDoS attacks on the application layer are latest and most problematic trends in network security threats. Botnet-based DDoS attacks on the application layer limits resources, curtails revenue, and yields customer dissatisfaction, among others. DDoS attacks are among the most difficult problems to resolve online, especially, when the target is the Web server. In this paper, we present a comprehensive study to show the danger of …
With a growing increase in botnet attacks, computer networks are constantly under threat from attacks that cripple cyber-infrastructure. Detecting these attacks in real-time proves to be a difficult and resource intensive task. One of the pertinent methods to detect such attacks is signature based detection using machine learning models. This paper explores the efficacy of these models at detecting botnet attacks, using data captured from large-scale network attacks. Our study provides a comprehensive overview of performance characteristics two machine learning models --- Random Forest and Mul…
Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of attackers is shown to be effective against a wide variety of attack types. Previous works, however, focus solely on anomalies in network traffic to detect bots and botnet. In this work we take a more robust approach of analyzing the heterogeneous events including network traffic, file download events, SSH logins and chain of commands input by attack…
Botnet is most widespread and occurs commonly in today's cyber attacks, resulting in serious threats to our network assets and organization's properties. Botnets are collections of compromised computers (Bots) which are remotely controlled by its originator (BotMaster) under a common Command-and-Control (C&C) infrastructure. They are used to distribute commands to the Bots for malicious activities such as distributed denial-of-service (DDoS) attacks, spam and phishing. Most of the existing Botnet detection approaches concentrate only on particular Botnet command and control (C&C) protocols (e.…
Botnets have become a serious security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic that's sweeping the planet, and the ever-larger number of cyber-criminals who now have access to or have developed increasingly more sophisticated tools are incentivizing the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make t…
Department of Computer Science, Faculty of Mathematics and Computer Science, and Interdisciplinary School for Doctoral Studies, University of Bucharest, Romania（布加勒斯特大学数学与计算机科学学院、跨学科博士研究学院）
With the raise in practice of Internet, in social, personal, commercial and other aspects of life, the cybercrime is as well escalating at an alarming rate. Such usage of Internet in diversified areas also augmented the illegal activities, which in turn, bids many network attacks and threats. Network forensics is used to detect the network attacks. This can be viewed as the extension of network security. It is the technology, which detects and also suggests prevention of the various network attacks. Botnet is one of the most common attacks and is regarded as a network of hacked computers. It c…
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitigate this new threat there is a need to develop new methods for detecting attacks launched from compromised IoT devices and differentiate between hour and millisecond long IoTbased attacks. In this paper we propose and empirically evaluate a novel network based anomaly detection method which extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic emanating from comprom…
Peer-to-Peer (P2P) botnets are becoming widely used as a low-overhead, efficient, self-maintaining, distributed alternative to the traditional client/server model across a broad range of cyberattacks. These cyberattacks can take the form of distributed denial of service attacks, authentication cracking, spamming, cyberwarfare or malware distribution targeting on financial systems. These attacks can also cross over into the physical world attacking critical infrastructure causing its disruption or destruction (power, communications, water, etc.). P2P technology lends itself well to being exploi…
The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices. Solutions and countermeasures are also provided: choosing a strong password, strong authentication mechanisms, check online databases of exposed or default cre…
The threats posed by botnets in the cyberspace continue to grow each day and it has become very hard to detect or infiltrate the cynicism of bots. This, is owing to the fact, that, the botnet developers each day, keep changing the propagation and attack techniques. Currently, most of these attacks have been centered on stealing computing energy, theft of personal information and Distributed Denial of Service (DDoS) attacks. In this paper, the authors propose a novel technique that uses the Non-Deterministic Polynomial-Time Hardness (NP-Hard Problem) based on the Traveling Salesperson Person (T…
Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）；University of Sydney（悉尼大学）
In this work, we present a lightweight IoT botnet detection solution, EDIMA, which is designed to be deployed at the edge gateway installed in home networks and targets early detection of botnets prior to the launch of an attack. EDIMA includes a novel two-stage Machine Learning (ML)-based detector developed specifically for IoT bot detection at the edge gateway. The ML-based bot detector first employs ML algorithms for aggregate traffic classification and subsequently Autocorrelation Function (ACF)-based tests to detect individual bots. The EDIMA architecture also comprises a malware traffic …
One of the most significant threats faced by enterprise networks today is from Bots. A Bot is a program that operates as an agent for a user and runs automated tasks over the internet, at a much higher rate than would be possible for a human alone. A collection of Bots in a network, used for malicious purposes is referred to as a Botnet. Bot attacks can range from localized attacks like key-logging to network intensive attacks like Distributed Denial of Service (DDoS). In this paper, we suggest a novel approach that can detect and combat Bots. The proposed solution adopts a two pronged strateg…
"These cameras are just like the Eye of Sauron": A Sociotechnical Threat Model for AI-Driven Smart Home Devices as Perceived by UK-Based Domestic Workers
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (PAN)（波兰科学院理论与应用信息学研究所）；Yaşar University（亚萨尔大学）
Botnet attacks are a major threat to networked systems because of their ability to turn the network nodes that they compromise into additional attackers, leading to the spread of high volume attacks over long periods. The detection of such Botnets is complicated by the fact that multiple network IP addresses will be simultaneously compromised, so that Collective Classification of compromised nodes, in addition to the already available traditional methods that focus on individual nodes, can be useful. Thus this work introduces a collective Botnet attack classification technique that operates on…
A number of Internet of Things (IoTs) platforms have emerged to enable various IoT apps developed by third-party developers to automate smart homes. Prior research mostly concerns the overprivilege problem in the permission model. Our work, however, reveals that even IoT apps that follow the principle of least privilege, when they interplay, can cause unique types of threats, named Cross-App Interference (CAI) threats. We describe and categorize the new threats, showing that unexpected automation, security and privacy issues may be caused by such threats, which cannot be handled by existing Io…
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization
L3I, University of La Rochelle, France（La Rochelle大学L3I实验室）；National Higher School of Technology and Engineering, LTSE, E3360100, Annaba, Algeria（阿尔及利亚安纳巴国家高等技术与工程学校）
The recent proliferation of smart home environments offers new and transformative circumstances for various domains with a commitment to enhancing the quality of life and experience. Most of these environments combine different gadgets offered by multiple stakeholders in a dynamic and decentralized manner, which in turn presents new challenges from the perspective of digital investigation. In addition, a plentiful amount of data records got generated because of the day to day interactions between these gadgets and homeowners, which poses difficulty in managing and analyzing such data. The anal…
This research aims to design an educational mobile game for home computer users to prevent from phishing attacks. Phishing is an online identity theft which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Mobile games could facilitate to embed learning in a natural environment. The paper introduces a mobile game design based on a story which is simplifying and exaggerating real life. We use a theoretical model derived from Technology Threat Avoidance Theory (TTAT) to address the …
For the smart vehicular network, we studied two technologies to realize it. The first technology is the cooperative scheme which improves capacity by properly combining the V2V and V2I. The second technology is an online learning algorithm which can deal with the beam selection problem in mmWave system. Both are effective and can be used in autonomous driving systems. However, advancements in the field of IoV have elicited research in different areas related to the field. This highlights a critical need to address security and protection challenges as a result of the progression of vehicles an…
As users increasingly introduce Internet-connected devices into their homes, having access to accurate and relevant cyber security information is a fundamental means of ensuring safe use. Given the paucity of information provided with many devices at the time of purchase, this paper engages in a critical study of the type of advice that home Internet of Things (IoT) or smart device users might be presented with on the Internet to inform their cyber security practices. We base our research on an analysis of 427 web pages from 234 organisations that present information on security threats and re…
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture
Department of Computer Science（计算机科学系）；Quaid-i-Azam University（夸迪·阿扎姆大学）；Cyber Security Center（网络安全中心）；Prince Mohammad Bin Fahd University（普林斯·穆罕默德·宾·法赫德大学）；School of Computing, Engineering and Built Environment（计算、工程与建筑环境学院）
University of Cagliari（卡利亚里大学）；Pluribus One S.r.l.（Pluribus One有限责任公司）
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker. This attack is made by leveraging the Domain Name System (DNS) technology through Domain Generation Algorithms (DGAs), a stealthy connection strategy that yet leaves suspicious data patterns. To detect such threats, advances in their analysis have been made. For the majority, they found Machin…
Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used and recommended, are no longer sufficient to meet the demands of growing security threats. As computing power increases and cost drops, Machine Learning is seen as an alternative method or an additional mechanism to defend against malwares, botnets, and other attacks. This paper explores Machi…
Smart homes are a special use-case of the Internet-of-Things (IoT) paradigm. Security and privacy are two prime concern in smart home networks. A threat-prone smart home can reveal lifestyle and behavior of the occupants, which may be a significant concern. This article shows security requirements and threats to a smart home and focuses on a privacy-preserving security model. We classify smart home services based on the spatial and temporal properties of the underlying device-to-device and owner-to-cloud interaction. We present ways to adapt existing security solutions such as distance-boundin…
Smart home devices have brought us many benefits such as advanced security, convenience, and entertainment. However, these devices also have made unintended consequences like giving ultimate power for devices' owners over their intimate partners in the same household which might lead to tech-facilitated domestic abuse (tech-abuse) as recent research has shown. In this paper, we systematize findings on tech-abuse in smart homes. We show that domestic abuse and Intimate Partner Violence (IPV) in smart homes is more effective and less risky for abusers. Victims find it more harmful and more chall…
Development and growth of Internet of Things (IoT) technology has exponentially increased over the course of the last 10 years since its inception, and as a result has directly influenced the popularity and size of smart homes. In this article we present the main technologies and applications that constitute a smart home, we identify the main security and privacy challenges that smart home face and we provide good practices to mitigate those threats.
Despite increasing uptake, there are still many concerns as to the security of virtual assistant hubs (such as Google Nest and Amazon Alexa) in the home. Consumer fears have been somewhat exacerbated by widely-publicised privacy breaches, and the continued prevalence of high-profile attacks targeting IoT networks. Literature suggests a considerable knowledge gap between consumer understanding and the actual threat environment; furthermore, little work has been done to compare which threat modelling approach(es) would be most appropriate for these devices, in order to elucidate the threats whic…
The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can …
The growing market for smart home IoT devices promises new conveniences for consumers while presenting novel challenges for preserving privacy within the home. Specifically, Internet service providers or neighborhood WiFi eavesdroppers can measure Internet traffic rates from smart home devices and infer consumers' private in-home behaviors. Here we propose four strategies that device manufacturers and third parties can take to protect consumers from side-channel traffic rate privacy threats: 1) blocking traffic, 2) concealing DNS, 3) tunneling traffic, and 4) shaping and injecting traffic. We …
The Internet of Things (IoT) is increasingly present in many family homes, yet it is unclear precisely how well families understand the cyber security threats and risks of using such devices, and how possible it is for them to educate themselves on these topics. Using a survey of 553 parents and interviews with 25 families in the UK, we find that families do not consider home IoT devices to be significantly different in terms of threats than more traditional home computers, and believe the major risks to be largely mitigated through consumer protection regulation. As a result, parents focus on…
Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise seventeen unsupervised anomaly detection algorithms on eleven attack datasets. R…
Smart speakers and voice-based virtual assistants are core components for the success of the IoT paradigm. Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic. To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information. This letter showcases the inadequacy of such countermeasures against machine learning attacks with a dedicated experimental campaign on a real network dataset. Results…
The Internet of Things (IoT) is already transforming industries, cities, and homes. The economic value of this transformation across all industries is estimated to be trillions of dollars and the societal impact on energy efficiency, health, and productivity are enormous. Alongside potential benefits of interconnected smart devices comes increased risk and potential for abuse when embedding sensing and intelligence into every device. One of the core problems with the increasing number of IoT devices is the increased complexity that is required to operate them safely and securely. This increase…
Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication. The rapid growth of VPA skill markets opens a new attack avenue, potentially allowing a remote adversary to publish attack skills to attack a large number of VPA users through popular IoT devices such as Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. More specifically, we implemented two new at…
Insecure connected devices can cause serious threats not just to smart home owners, but also the underlying infrastructural network as well. There has been increasing academic and regulatory interest in addressing cybersecurity risks from both the standpoint of Internet of Things (IoT) vendors and that of end-users. In addition to the current data protection and network security legal frameworks, for example, the UK government has initiated the 'Secure by Design' campaign. While there has been work on how organisations and individuals manage their own cybersecurity risks, it remains unclear to…
Smartphones with the platforms of applications are gaining extensive attention and popularity. The enormous use of different applications has paved the way to numerous security threats. The threats are in the form of attacks such as permission control attacks, phishing attacks, spyware attacks, botnets, malware attacks, privacy leakage attacks. Moreover, other vulnerabilities include invalid authorization of apps, compromise on the confidentiality of data, invalid access control. In this paper, an application-based attack modeling and attack detection is proposed. Due to A novel attack vulnera…
As IoT deployments grow in scale for applications such as smart cities, they face increasing cyber-security threats. In particular, as evidenced by the famous Mirai incident and other ongoing threats, large-scale IoT device networks are particularly susceptible to being hijacked and used as botnets to launch distributed denial of service (DDoS) attacks. Real large-scale datasets are needed to train and evaluate the use of machine learning algorithms such as deep neural networks to detect and defend against such DDoS attacks. We present a dataset from an urban IoT deployment of 4060 nodes descr…
